Task Information

Total Number of Trials = 104

Sweet taste and bitter taste was selected by participant to reflect reward & punishment

Learning Curves for each Shape Pair

all_plot<-ggarrange(plot1, plot2, plot3, 
          labels = c("80/20 pair", "70/30 pair", "60/40 pair"), 
          ncol = 3, nrow = 1)
Removed 2 rows containing missing values (geom_path).Removed 2 rows containing missing values (geom_point).Removed 3 rows containing missing values (geom_path).Removed 3 rows containing missing values (geom_point).
all_plot

Learning Curves Together

plot4

Begin Processing Graphs for Each Participant

Plot “Heatmaps” of Outcomes During Training

mydata$outcome0[mydata$outcome == "Miss"] <- 0
Unknown or uninitialised column: 'outcome0'.
mydata$outcome0[mydata$outcome == "punish"] <- -10
mydata$outcome0[mydata$outcome == "reward"] <- 10
hmTOTAL<-ggplot(mydata,aes(as.numeric(Count), as.factor(sub_num) , fill=outcome0))+
  geom_tile()+
  scale_fill_gradient2(low="red", high="green", na.value="black", name="")+
  theme_classic()+ xlab(label = "Trial") + ylab(label= 'Subject Number')+
  guides(fill=guide_legend(title='Outcome'))
  #geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))

Plot “Heatmaps” of Outcomes During Training

-10 (red) = punishment (bitter taste)

0 (white) = missed press (no taste)

10 (green) = reward (sweet taste)

hmTOTAL

Overlay with Shape Selected & Outcome

hmTOTAL1

Split Into Groups Based on Posttest Performance

summary(data0$sensitivity_reward)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.2917  0.4444  0.5000  0.5096  0.5789  0.7500     104 
data0$learn[data0$sensitivity_reward < 0.444]<- "didn't learn"
data0$learn[data0$sensitivity_reward >= 0.444 & data0$sensitivity_reward < 0.5  ]<- "maybe learn"
data0$learn[ data0$sensitivity_reward >= 0.5 & data0$sensitivity_reward < 0.57 ]<- "ok"
data0$learn[ data0$sensitivity_reward >= 0.57]<- "pretty good"
summary(as.factor(data0$learn))
didn't learn  maybe learn           ok  pretty good         NA's 
        2211         1541         2609         2643          104 
hmTOTALgood<-ggplot(subset(data0, learn == "pretty good"),aes(as.numeric(Count), as.factor(sub_num) ,fill=outcome0))+
  geom_tile()+
  scale_fill_gradient2(low="red", high="green", na.value="black", name="") +
  theme_classic()+ xlab(label = "Trial") + ylab(label= 'Subject Number') +
  guides(fill=guide_legend(title='Outcome')) 
  #geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
#hmTOTALgood
hmTOTALbad<-ggplot(subset(data0, learn == "didn't learn"),aes(as.numeric(Count), as.factor(sub_num) ,fill=outcome0))+
  geom_tile()+
  scale_fill_gradient2(low="red", high="green", na.value="black", name="") +
  theme_classic()+ xlab(label = "Trial") + ylab(label= 'Subject Number') +
  guides(fill=guide_legend(title='Outcome')) 
  #geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
#hmTOTALbad
test1<-ggarrange(hmTOTALgood,hmTOTALbad, 
          labels = c("Good Posttest", "Bad Posttest"), 
          ncol = 1, nrow = 2)

Split Into Groups Based on Posttest Performance

test1

Plot “Heatmaps” of Choices During Training

-30 (pink) = Choose F (40% correct)

-20 (mid pink) = Choose D (30% correct)

-10 (light pink) = Choose B (20% correct)

0 (white) = missed press (no choice)

10 (light blue) = Choose E (60% correct)

20 (mid blue) = Choose C (70% correct)

30 (blue) = Choose A (80% correct)

test2

Are there differences in training between posstest groups?

mytable <- xtabs(~choice+learn, data=data0)
ftable(mytable) # print table 
       learn didn't learn maybe learn  ok pretty good
choice                                               
A                     362         248 417         460
B                     331         242 420         441
C                     359         236 401         425
D                     353         240 413         377
E                     332         224 410         407
F                     358         270 384         418
Miss                  116          81 164         115
summary(mytable) # chi-square test of indepedence
Call: xtabs(formula = ~choice + learn, data = data0)
Number of cases in table: 9004 
Number of factors: 2 
Test for independence of all factors:
    Chisq = 23.464, df = 18, p-value = 0.1734
mytable <- xtabs(~outcome+learn, data=data0)
ftable(mytable) # print table 
        learn didn't learn maybe learn   ok pretty good
outcome                                                
Miss                   116          81  164         115
punish                1032         717 1218        1227
reward                1063         743 1227        1301
summary(mytable) # chi-square test of indepedence
Call: xtabs(formula = ~outcome + learn, data = data0)
Number of cases in table: 9004 
Number of factors: 2 
Test for independence of all factors:
    Chisq = 10.656, df = 6, p-value = 0.09961
mytable <- xtabs(~congruent+learn, data=data0)
ftable(mytable) # print table 
           learn didn't learn maybe learn   ok pretty good
congruent                                                 
matched                  1489         997 1734        1777
mismatched                606         463  711         751
Miss                      116          81  164         115
summary(mytable) # chi-square test of indepedence
Call: xtabs(formula = ~congruent + learn, data = data0)
Number of cases in table: 9004 
Number of factors: 2 
Test for independence of all factors:
    Chisq = 13.713, df = 6, p-value = 0.03301

Ah ha moment

There is a difference between the number of mismatched trials in the “learners” and “non learners.” Those who “don’t learn” have more mismatches.

Histograms

---
title: "Bevel Probabilistic Selection Task Performance"
output:
  word_document: default
  html_notebook: default
  html_document:
    df_print: paged
---
## Task Information
#Total Number of Trials = 104 
#Sweet taste and bitter taste was selected by participant to reflect reward & punishment
#
#

```{r, echo=FALSE, results='hide', message=FALSE}
library(dplyr)
library(tidyr)
library(ggplot2)
library(reshape)
library(ggpubr)

data <- read.delim("~/Documents/bevel_choice/all_subjects.txt")
names(data) <- c("subj", "run", "pair", "choice", "outcome", "congruent", "RT")

data <- data %>%
  group_by(.dots=c("subj","pair")) %>%
  mutate(Count=row_number())

percent_correct_by_trial_ab <- function(n) {
  count <- sum(data$Count == n & data$choice=="corr" & data$pair == "12")
  countall <- sum(data$Count == n & data$pair == "12")
  return(count/countall)
}

percent_correct_by_trial_cd <- function(n) {
  count <- sum(data$Count == n & data$choice=="corr" & data$pair == "34")
  countall <- sum(data$Count == n & data$pair == "34")
  return(count/countall)
}

percent_correct_by_trial_ef <- function(n) {
  count <- sum(data$Count == n & data$choice=="corr" & data$pair == "56")
  countall <- sum(data$Count == n & data$pair == "56")
  return(count/countall)
}

#percent_correct_by_trial_ab(10)

x <- 1:46
output_ab <- lapply(x, percent_correct_by_trial_ab) 
output_cd <- lapply(x, percent_correct_by_trial_cd) 
output_ef <- lapply(x, percent_correct_by_trial_ef) 

df_ab <- data.frame(matrix(unlist(output_ab), nrow=length(output_ab), byrow=T))
df_cd <- data.frame(matrix(unlist(output_cd), nrow=length(output_cd), byrow=T))
df_ef <- data.frame(matrix(unlist(output_ef), nrow=length(output_ef), byrow=T))

colnames(df_ab)[colnames(df_ab)=="matrix.unlist.output_ab...nrow...length.output_ab...byrow...T."] <- "percent_correct_ab"
colnames(df_cd)[colnames(df_cd)=="matrix.unlist.output_cd...nrow...length.output_cd...byrow...T."] <- "percent_correct_cd"
colnames(df_ef)[colnames(df_ef)=="matrix.unlist.output_ef...nrow...length.output_ef...byrow...T."] <- "percent_correct_ef"

df_ab$trialnum<-row.names(df_ab)
df_cd$trialnum<-row.names(df_cd)
df_ef$trialnum<-row.names(df_ef)
#head(df_ab$trialnum)

data0<-merge(df_ab, df_cd, by="trialnum")
data1<- merge(data0, df_ef, by="trialnum")

df_ab$trial <- seq.int(nrow(df_ab))
df_cd$trial <- seq.int(nrow(df_cd))
df_ef$trial <- seq.int(nrow(df_ef))

```


```{r, echo=FALSE, results='hide', message=FALSE}
plot1 <- ggplot(data=df_ab, aes(x=trial, y=percent_correct_ab, group=1)) +
  geom_line()+
  geom_point() +
  theme_classic() + scale_x_continuous(name="Trial Number") +
  scale_y_continuous(name="Percent of Sample choosing 80% Shape")

plot2 <- ggplot(data=df_cd, aes(x=trial, y=percent_correct_cd, group=1)) +
  geom_line()+
  geom_point() + 
  theme_classic() + scale_x_continuous(name="Trial Number") +
  scale_y_continuous(name="Percent of Sample choosing 70% Shape") 

plot3 <- ggplot(data=df_ef, aes(x=trial, y=percent_correct_ef, group=1)) +
  geom_line()+
  geom_point() + 
  theme_classic() + scale_x_continuous(name="Trial Number") +
  scale_y_continuous(name="Percent of Sample choosing 60% Shape")
```

#Learning Curves for each Shape Pair
```{r, message=FALSE, fig.width=8}
all_plot<-ggarrange(plot1, plot2, plot3, 
          labels = c("80/20 pair", "70/30 pair", "60/40 pair"), 
          ncol = 3, nrow = 1)

all_plot
```



```{r, echo=FALSE, results='hide', message=FALSE}
plot4 <- ggplot(data1, aes(as.numeric(trialnum))) + 
  geom_line(aes(y = percent_correct_ab, colour = "80/20 pair")) + 
  geom_line(aes(y = percent_correct_cd, colour = "70/30 pair")) + 
  geom_line(aes(y = percent_correct_ef, colour = "60/40 pair")) +
  theme_classic() + scale_x_continuous(name="Trial Number") +
  scale_y_continuous(name="Percent of Sample choosing Higher %Correct Shape") +
  labs(colour = "Shape Pair")
  
```


# Learning Curves Together
```{r}
plot4
```


#Begin Processing Graphs for Each Participant
```{r, , echo=FALSE, results='hide',message=FALSE}
library(ggplot2)
library(ggpubr)
library(plyr)
library(tidyverse)
library(reshape)
library(data.table)
```

```{r, , echo=FALSE, results='hide',message=FALSE}
readdata <- function(fn){
    dt_temp <- fread(fn, sep="\t")
    return(dt_temp)
}

all.files <- list.files(path = "~/Documents/bevel_choice/by_participant_txtfiles/",pattern = ".txt", full.names = TRUE)
mylist <- lapply(all.files, readdata)
mydata <- rbindlist(mylist, use.names=FALSE)

names(mydata)<-c("sub_num","run","type","choice","side","outcome","congruent","RT")
mydata$side<-as.factor(mydata$side)
mydata$side<-revalue(mydata$side, c("1"="left", "2"="right"))

mydata <- mydata %>%
 group_by(.dots=c("sub_num")) %>%
 dplyr::mutate(Count=row_number())
```

# Plot "Heatmaps" of Outcomes During Training
```{r, fig.width=8, fig.height=11}
mydata$outcome0[mydata$outcome == "Miss"] <- 0
mydata$outcome0[mydata$outcome == "punish"] <- -10
mydata$outcome0[mydata$outcome == "reward"] <- 10

hmTOTAL<-ggplot(mydata,aes(as.numeric(Count), as.factor(sub_num) , fill=outcome0))+
  geom_tile()+
  scale_fill_gradient2(low="red", high="green", na.value="black", name="")+
  theme_classic()+ xlab(label = "Trial") + ylab(label= 'Subject Number')+
  guides(fill=guide_legend(title='Outcome'))
  #geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
```

# Plot "Heatmaps" of Outcomes During Training
##-10 (red) = punishment (bitter taste)
##0 (white) = missed press (no taste)
##10 (green) = reward (sweet taste)
```{r}
hmTOTAL
```

Overlay with Shape Selected & Outcome
```{r, echo=FALSE, results='hide', message=FALSE}
hmTOTAL1<-ggplot(mydata,aes(as.numeric(Count), as.factor(sub_num) , fill=outcome0))+
  geom_tile()+
  scale_fill_gradient2(low="red", high="green", na.value="black", name="")+
  theme_classic()+ xlab(label = "Trial") + ylab(label= 'Subject Number')+
  guides(fill=guide_legend(title='Outcome')) +
  geom_point(aes(shape=as.factor(choice), size=.7, color=as.factor(choice)))


```

```{r, fig.width=20, fig.height=16}
#hmTOTAL1
```


```{r, echo=FALSE, results='hide',message=FALSE}
more_data<-read.table("~/Documents/bevel_choice/clean_bevel.csv",header=T, sep=",")
more_data$sub_num<-row.names(more_data)
head(more_data$sub_num)

data0<-merge(mydata, more_data, by="sub_num")

```

#Split Into Groups Based on Posttest Performance
```{r, fig.width=8, fig.height=11}
summary(data0$sensitivity_reward)
data0$learn[data0$sensitivity_reward < 0.444]<- "didn't learn"
data0$learn[data0$sensitivity_reward >= 0.444 & data0$sensitivity_reward < 0.5  ]<- "maybe learn"
data0$learn[ data0$sensitivity_reward >= 0.5 & data0$sensitivity_reward < 0.57 ]<- "ok"
data0$learn[ data0$sensitivity_reward >= 0.57]<- "pretty good"
summary(as.factor(data0$learn))

hmTOTALgood<-ggplot(subset(data0, learn == "pretty good"),aes(as.numeric(Count), as.factor(sub_num) ,fill=outcome0))+
  geom_tile()+
  scale_fill_gradient2(low="red", high="green", na.value="black", name="") +
  theme_classic()+ xlab(label = "Trial") + ylab(label= 'Subject Number') +
  guides(fill=guide_legend(title='Outcome')) 
  #geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
#hmTOTALgood


hmTOTALbad<-ggplot(subset(data0, learn == "didn't learn"),aes(as.numeric(Count), as.factor(sub_num) ,fill=outcome0))+
  geom_tile()+
  scale_fill_gradient2(low="red", high="green", na.value="black", name="") +
  theme_classic()+ xlab(label = "Trial") + ylab(label= 'Subject Number') +
  guides(fill=guide_legend(title='Outcome')) 
  #geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
#hmTOTALbad

test1<-ggarrange(hmTOTALgood,hmTOTALbad, 
          labels = c("Good Posttest", "Bad Posttest"), 
          ncol = 1, nrow = 2)

```


#Split Into Groups Based on Posttest Performance
```{r, fig.width=8, fig.height=11}
test1
```


```{r, echo=FALSE, results='hide',message=FALSE}
data0$choice0[data0$choice == "Miss"] <- 0
# good
data0$choice0[data0$choice == "A"] <- 30
data0$choice0[data0$choice == "C"] <- 20
data0$choice0[data0$choice == "E"] <- 10
# bad
data0$choice0[data0$choice == "B"] <- -30
data0$choice0[data0$choice == "D"] <- -20
data0$choice0[data0$choice == "F"] <- -10

hmTOTALgood_flip<-ggplot(subset(data0, learn == "pretty good"),aes(as.numeric(Count), as.factor(sub_num) ,fill=choice0))+
  geom_tile()+
  scale_fill_gradient2(low="pink", high="blue", na.value="black", name="") +
  theme_classic()+ xlab(label = "Trial") + ylab(label= 'Subject Number')+
  guides(fill=guide_legend(title='Choice')) 
  #geom_point(aes(shape=as.factor(outcome), size=1, color=as.factor(outcome)))
#hmTOTALgood_flip


hmTOTALbad_flip<-ggplot(subset(data0, learn == "didn't learn"),aes(as.numeric(Count), as.factor(sub_num) ,fill=choice0))+
  geom_tile()+
  scale_fill_gradient2(low="pink", high="blue", na.value="black", name="") + 
  theme_classic()+ xlab(label = "Trial") + ylab(label= 'Subject Number') +
  guides(fill=guide_legend(title='Choice')) 
  #geom_point(aes(shape=as.factor(outcome), size=1, color=as.factor(outcome)))
#hmTOTALbad_flip

test2<-ggarrange(hmTOTALgood_flip,hmTOTALbad_flip, 
          labels = c("Good Posttest", "Bad Posttest"),
          ncol = 1, nrow = 2)

```

#Plot "Heatmaps" of Choices During Training
##-30 (pink) = Choose F (40% correct)
##-20 (mid pink) = Choose D (30% correct)
##-10 (light pink) = Choose B (20% correct)
##0 (white) = missed press (no choice)
##10 (light blue) = Choose E (60% correct)
##20 (mid blue) = Choose C (70% correct)
##30 (blue) = Choose A (80% correct)

```{r, fig.width=8, fig.height=11}
test2
```

#Are there differences in training between posstest groups? 
```{r}
mytable <- xtabs(~choice+learn, data=data0)
ftable(mytable) # print table 
summary(mytable) # chi-square test of indepedence

mytable <- xtabs(~outcome+learn, data=data0)
ftable(mytable) # print table 
summary(mytable) # chi-square test of indepedence


mytable <- xtabs(~congruent+learn, data=data0)
ftable(mytable) # print table 
summary(mytable) # chi-square test of indepedence

```
# Ah ha moment
#There is a difference between the number of mismatched trials in the "learners" and "non learners." Those who "don't learn" have more mismatches.

#Histograms
